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Targeting the common functions of the Zhejiang Library website, elderly individuals were invited to complete six experimental tasks on the improved website interfaces, and subjective data (PAD emotion scale and usability evaluation) and objective data (eye movement data) were recorded to verify the effects of graphic layout and navigation position on the information-seeking experience of elderly individuals.

This study analyzes the effect of the graphic layout and navigation position of the Zhejiang Library’s website interface on the emotional state, perceived usability and information-seeking time of elderly individuals, with the aim of providing guidance and suggestions for the elderly-oriented reform of the public library website.

The experimental results show that the graphic layout has a significant effect on the emotional state and perceived usability of elderly individuals, and the navigation position has a significant effect on the information-seeking time; the interaction between graphic layout and navigation position exerts a significant effect on the information-seeking time of elderly individuals. The eye movement data show that elderly individuals have a better information-seeking experience when the top navigation bar and image-text matched arrangement are used for the interface layout.

This study adopts a new approach combining subjective data and eye movement data to evaluate the effect of the public library website’s interface layout on the information-seeking experience for older people. The findings can provide a theoretical basis and methodological support for the elderly-oriented reform of public library websites. They can also provide scientific design suggestions for age-friendly interface layouts of other Internet products and service applications.

This study adopted the collaborative consumption triangle to explore the influence of online food delivery platforms (OFDP) on consumer purchase intentions. It investigates the effects of restaurants' corporate social responsibility (CSR) practices, individuals' food neophilic tendencies (FNT), and platforms' perceived benefits on purchase intention within OFDP. Furthermore, the study analyses differences in consumers' pro-environmental behaviour (PEB) on OFDP.

The 497 participants conducted a web-based self-completion survey, using structural equation modelling to analyse the path structure of consumer purchasing intention. Furthermore, differences in PEB among OFDP consumers were compared through multigroup analysis.

The findings indicate that CSR influences the perceived value of sustainability and that the perceived value of sustainability influences purchase intention. Additionally, the influence of the perceived value of sustainability on purchase intention is more pronounced among consumers with low PEB compared to those with high PEB.

The findings may not be generalisable to other countries due to cultural differences, CSR policies, and strategies for promoting sustainable development.

The study provides valuable contributions related to (1) restaurants increasing their revenue and meeting their long-term sustainable development goals; (2) providing reusable containers policy and reusable containers policy and category tags for restaurants within OFDP.

This study is a pioneering work examining factors influencing purchase intentions within OFDP from the tripartite collaborative consumption perspective post-COVID-19 and focuses on the differences in PEB concerning OFDP.

Data transformation has prompted enterprises to rethink their strategic development. Scholars have frequently acknowledged the vast potential value of supply chain data and realised that simply owning data resources cannot guarantee excellent innovation performance (IP). Therefore, this study focussed on the mediating and moderating issues between data-driven supply chain orientation (DDSCO) and IP. More specifically, the purpose was to explore (1) whether DDSCO promotes enterprise innovation through dynamic and improvisational capabilities and (2) how information complexity (INC) plays a moderating role between capabilities and performance.

An empirical study was performed using the results of a questionnaire survey, and a literature review was used to build the premises of this study. A sample was conducted on 296 Chinese enterprises, and the data collected were used to test the hypothesis by successive regression.

This research has implications for the theoretical development of DDSCO, as well as the dynamic capabilities (DC) and improvisation capabilities (IC) in innovation strategic literature. The empirical results show that DDSCO has a direct, positive impact on both DC and IC, which thus positively impact IP. Meanwhile, IC has a negative moderating effect on the path joining DC and IP. Conversely, IC has a positive moderating effect on the path joining IC and IP.

Although this study has limitations, it also creates opportunities for future research. The survey comes from different industries, so the possibility of unique influences within industries cannot be ruled out. Second, the authors' survey is based on cross-sectional data, which allow for more comprehensive data verification in the future. Third, this study also provides opportunities for future research, because it proves that DC and IC, as partial mediators of DDSCO and IP, can mine other paths of the data-driven supply chain in IP. For example, the perspective of the relationship between supply chain members, knowledge perspective, etc.

The research findings offer a novel perspective for enterprise managers. First, enterprises can leverage supply chain data to gain competitive advantages in innovation. Second, it is imperative for enterprises to acknowledge the significance of developing dynamic and IC. This also requires enterprises to acknowledge innovations in DDSCO necessitate a focus on dynamic and IC. Third, it is recommended that managers take into account both sides of IC and encourage enterprises to prioritise the utilisation of IC.

Empirical research results revealed how DDSCO improves IP and is an extension of digital transformation in the supply chain field, providing new opportunities and challenges for enterprise innovation. It can also expand the enterprise's understanding of DDSCO. Second, based on resource-based theory, it is possible to develop and test theoretical arguments regarding the importance of dynamic and IC as intermediaries in the DDSCO-IP. Third, the authors conducted simulations of highly dynamic data environments to develop and test theoretical arguments about the importance of IC as a moderator of capabilities-performance relationships.

This paper proposes a solution for recognizing human faces under mask-wearing. The lower part of human face is occluded and could not be used in the learning process of face recognition. So, the proposed solution is developed to recognize human faces on any available facial components which could be varied depending on wearing or not wearing a mask.

The proposed solution is developed based on the FaceNet framework, aiming to modify the existing facial recognition model to improve the performance of both scenarios of mask-wearing and without mask-wearing. Then, simulated masked-face images are computed on top of the original face images, to be used in the learning process of face recognition. In addition, feature heatmaps are also drawn out to visualize majority of parts of facial images that are significant in recognizing faces under mask-wearing.

The proposed method is validated using several scenarios of experiments. The result shows an outstanding accuracy of 99.2% on a scenario of mask-wearing faces. The feature heatmaps also show that non-occluded components including eyes and nose become more significant for recognizing human faces, when compared with the lower part of human faces which could be occluded under masks.

The convolutional neural network based solution is tuned up for recognizing human faces under a scenario of mask-wearing. The simulated masks on original face images are augmented for training the face recognition model. The heatmaps are then computed to prove that features generated from the top half of face images are correctly chosen for the face recognition.

The purpose of this study is to propose and test an extended theory of planned behavior model to explain the significance of social media influencers’ credibility, social media usage (SMU) and social media marketing while simultaneously examining females’ intentions to purchase cosmetics online.

An online questionnaire was used to collect data from 386 female consumers of cosmetics, and the data were analyzed using partial least squares structural equation modeling (PLS-SEM) with the help of SmartPLS version 4.

First, the results indicated that SMU, social media influencers’ credibility and social media marketing significantly and positively impact the theory of planned behavior components, namely, attitude, subjective norms (SNs) and perceived behavioral control (PBC). Second, results confirmed that attitude, SNs and PBC significantly and positively influenced female consumers’ online purchase intentions (OPIs).

To the best of the authors’ knowledge, this study is one of the first to examine an integrated model that consisted of SMU, social media marketing and social media influencers along with the theory of planned behavior constructs in their proposed research model in the context of cosmetics in India. The study also enriched the body of knowledge about using the PLS-SEM approach to predict OPI for cosmetics.